A Geological Borehole Data Protection Based on Graph Neural Networks

被引：0

作者：

Shang H. ^{[1
]}

Zhu H. ^{[1
,2
]}

Li S. ^{[1
]}

Song X. ^{[3
]}

Xia Y. ^{[4
]}

Liu H. ^{[4
]}

Yang F. ^{[4
]}

机构：

[1] Shandong Institute of Geological Survey, Jinan

[2] School of Environmental Studies, China University of Geosciences, Wuhan

[3] Shandong Provicial Archives of Natural Resources, Jinan

[4] School of Computer Science, China University of Geosciences, Wuhan

来源：

Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences | 2023年 / 48卷 / 08期

关键词：

data protection for geological drilling data; deep learning; figure adversarial attack; graph attention network; graph neural network; interpretability;

D O I：

10.3799/dqkx.2021.232

中图分类号：

学科分类号：

摘要：

With the development of deep learning technology, attackers can obtain potentially sensitive information from public geological data through classification, prediction, and other methods, which could lead to the leakage of important geological data. To solve the above problems, we propose a geological drilling data protection model based on graph adversarial attack Gcntack. Based on the degree properties of geological data topology, we first generate attacks that satisfy the same power-law distribution as tiny node disturbance. It can ensure that the adversarial attacks are not easy to be found, and while can change the classification result of the target node. Secondly, we introduce an attention mechanism. Using a graph attention network model based on interpretability, we analyze the properties of key nodes that directly affect the results of the adversarial attacks, so as to verify the rationality of the selecting adversarial nodes in the Gcntack model. Finally, a comprehensive evaluation, based on the benchmark dataset and geological drilling dataset, is presented to show this proposed scheme can reduce the prediction accuracy of attackers and achieve the purpose of protecting important geological drilling data. © 2023 China University of Geosciences. All rights reserved.

引用

页码：3151 / 3161

页数：10

共 31 条

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